class_docstr: Ranked List Loss described in https://arxiv.org/abs/1903.03238
     Default parameters correspond to RLL-Simpler, preferred for exploratory analysis.
  
  Args:
      * margin (float): margin between positive and negative set
      * imbalance (float): tradeoff between positive and negative sets. As the name suggests this takes into account
                          the imbalance between positive and negative samples in the dataset
      * alpha (float): smallest distance between negative points
      * Tp & Tn (float): temperatures for, respectively, positive and negative pairs weighting.
code: def __init__(
      self, margin, Tn, imbalance=0.5, alpha=None, Tp=0, **kwargs
  ):
      super().__init__(**kwargs)
  
      self.margin = margin
  
      assert 0 <= imbalance <= 1, "Imbalance must be between 0 and 1"
      self.imbalance = imbalance
  
      if alpha is not None:
          self.alpha = alpha
      else:
          self.alpha = 1 + margin / 2
  
      if Tp > 5 or Tn > 5:
          warnings.warn(
              "Values of Tp or Tn are too high. Too large temperature values may lead to overflow."
          )
  
      self.Tp = Tp
      self.Tn = Tn
      self.add_to_recordable_attributes(
          list_of_names=["imbalance", "alpha", "margin", "Tp", "Tn"],
          is_stat=False,
      )
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/ranked_list_loss.py
imports: import warnings
  import torch
  from ..distances import LpDistance
  from ..utils import common_functions
  from .base_metric_loss_function import BaseMetricLossFunction
label_desc: Use image node_id 1 to create a new RankedListLoss object from inherited base classes: BaseMetricLossFunction with example: obj = RankedListLoss(margin, Tn, imbalance, alpha, Tp)